Is the first-trimester combined screening result associated with the phenotype of Down syndrome? A population-based cohort study

Objective

To investigate if the Down syndrome phenotype differs according to the result of first-trimester combined screening (FTS).

Method

We included all Down syndrome cases diagnosed by karyotype in pregnancy or after birth in Denmark during 2005–2018. We compared screen positive (odds ≥1:300) and screen negative (odds <1:300) cases as well as screen result subgroups with respect to anthropometrics, congenital malformations, childhood diseases, and hospitalization.

Results

Absolute measures of fetal and birth anthropometrics were comparable between groups. A prenatal malformation diagnosis was more prevalent among screen positive than screen negative cases. Analyses suggested that this could reflect a detection bias. Cases with a screen result of 1:2–1:10 had a higher probability of being diagnosed with a malformation prenatally and with severe congenital heart disease (CHD) postnatally compared with a result of 1:11–1:300. Screen positive cases more often had non-severe CHD but less often a non-heart malformation compared with screen negative cases, while proportions of severe CHD were similar in these groups. Data on hospitalizations showed inconsistent results.

Conclusion

The 1:300 screening threshold had limited or no value in predicting Down syndrome phenotype severity. In contrast, cases with a screen result between 1:2 and 1:10 may represent a more severe phenotype.     

瑞典区域气候模拟计划(SWECLIM)综述

瑞典区域气候模拟计划(SWECLIM)是一个历时6年半的国家研究网络计划,该研究网络主要涉及区域气候模拟、区域气候变化预测以及水文影响评估,并且为广大的利益相关人士提供相应信息.大部分研究活动主要针对北欧区域气候系统.最终促使建立一个先进的大气-海洋-水文相耦合的区域气候模型系统和一套区域气候变化预测体系,并促成了相关数据的积累和相关过程研究的进步.反过来,作为对不同社会部门影响分析的一个起点,这些也可以用来作为信息源和为教育服务,而且还为国际气候研究做出了贡献.     

Modelling the global cycle of carbon,nitrogen and phosphorus and their influence on the global heat balance

A model for the accumulation of CO₂ in the atmosphere has been set up, taking into consideration: (1) the global cycle of nitrogen and phosphorus: (2) the CO₂-diffusion in the oceans with means of a multilayer ?ea model; (3) the ability of the oceans to take up CO₂; (4) the influence of the CO₂-concentrations and of the temperature on this ability; (5) different growth rates for the consumption of fossil fuel including logistic growth; (6) the natural climatic variation.It is shown to be essential to include all these factors. The inclusion of factors (4) and (6), which have been omitted in many previous publications, is very essential to the model.     

Using multi-objective classification to model communities of soil microarthropods

In agricultural soil, a suite of anthropogenic events shape the ecosystem processes and populations. However, the impact from anthropogenic sources on the soil environment is almost exclusively assessed for chemicals, although other factors like crop and tillage practices have an important impact as well. Thus, the farming system as a whole should be evaluated and ranked according to its environmental benefits and impacts. Our starting point is a data set describing agricultural events and soil biological parameters. Using machine learning methods for inducing regression and model trees, we produce empirical models able to predict the soil quality from agricultural measures in terms of quantities describing the soil microarthropod community. We are also interested in discovering additional higher level knowledge. In particular, we have identified the most important factors influencing the population densities of springtails and mites and their biodiversity. We also identify to which agricultural actions different microarthropods react distinctly. To obtain this higher level knowledge, we employ multi-objective regression trees.